This disclosure relates generally to servicing products and systems and more particularly to automatically predicting the timing and costs of future service events of a product or a system.
The market for long-term contractual agreements has grown at high rates over recent years for many of today""s service organizations. As the service organizations establish long-term contractual agreements with their customers, it becomes important to understand the expected costs and risks associated with the pricing of service contracts and portfolio management of the contracts. In addition, the service organizations need to have an understanding of the planning of repairs (shop workload planning) and how the introduction of new technology will affect their service contracts. In order to analyze these issues, it is necessary to correctly model the underlying behavior of the product or system so that each can be serviced in the most cost-effective manner.
Currently available analytical practices are unable to accurately model service requirements for complex products or systems. Typically, these models contain poor cost information which result in the service organization inefficiently managing the risk associated with their service portfolios, failing to respond to customer needs and new technology, which all lead to lower long-term contract profitability. A standard time-series method is one particular approach that has been used to model the service requirements of repairable systems such as aircraft engines, automobiles, locomotives and other high tech products. This time-series method examines historical data obtained over a five to ten year period and forms a trend line on either system costs and/or number of repairs made to the system. The trend line is then used to predict future costs and number of repairs. A limitation with this time series method is that it does not give details of failures at a compartmental level. A compartment is a physical or performance related sub-system of the repairable product, which when it fails causes the product to require maintenance or servicing. Other limitations with the standard time series method is that it does not account for the life cycle of the repairable product and thus does not provide a distribution of the expected service events for the product. An analysis based on engineering relationships to determine compartment parameters is another method used to model the service requirements of repairable systems. A limitation with this analysis is that it is not well based in underlying statistics, and thus cannot be shown to accurately model the repairable product on an ongoing basis.
In order to overcome the above problems, there is a need for an approach that can model the service requirements of repairable systems that is accurate and has a comprehensive statistical framework. Such an approach will lead to better cost projections, more realistic and effective risk management, new technology introduction and day-to-day service that is more responsive to customer needs and higher long-term contract profitability.
In accordance with one embodiment of this disclosure, there is an automated system for predicting the timing and costs of future service events in a life cycle of a product formed from a plurality of compartments. The system comprises a database that contains a plurality of service information and a plurality of performance information for the product. A statistical analyzer automatically analyzes the plurality of service information to determine a plurality of compartment failure information. A performance deterioration rate automatically analyzes performance deterioration rate of the product from the plurality of service information and performance information. A simulator, automatically simulates a distribution of future service events in the life cycle of the product and predicts the costs of the service events according to the plurality of compartment failure information and the performance deterioration rate analysis.
Similarly, in this disclosure there is a method for automatically predicting the timing and costs of future service events in a life cycle of a product formed from a plurality of compartments. The method comprises storing a plurality of service information and a plurality of performance information for the product; automatically analyzing the plurality of service information to determine a plurality of compartment failure information; automatically performing a deterioration rate analysis that determines performance deterioration rate of the product from the plurality of service information and performance information; and automatically simulating a distribution of future service events in the life cycle of the product and predicting the costs of the service events according to the plurality of compartment failure information and the performance deterioration rate analysis.
Also, in this disclosure there is a computer-readable medium storing computer instructions for instructing a computer system to automatically predict the timing and costs of future service events in a life cycle of a product formed from a plurality of compartments. The computer instructions comprise storing a plurality of service information and a plurality of performance information for the product; automatically analyzing the plurality of service information to determine a plurality of compartment failure information; automatically performing a deterioration rate analysis of the product from the plurality of service information and performance information and automatically simulating a distribution of future service events in the life cycle of the product and predicting the costs of the service events according to the plurality of compartment failure information and the performance deterioration rate analysis.